期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：10
出版社：Journal of Theoretical and Applied
摘要：Classification in data mining is the most pervasive problem in the distributed computing environment. It is really challenging to classify the data residing on different sites without revealing the private data to each other. However, decision tree classifier has become a good solution to reduce this problem. This paper explains how to build a privacy preserving decision tree over a vertically partitioned data. The decision tree is created on the data which is partitioned vertically and is owned by different parties along with concealing data of different parties. The proposed algorithm uses a best and most efficient splitting strategy for attributes and at the same time a semi-honest third party is also used. It helps different parties in calculation for construction of decision tree. Scalar product protocol and secure multi-party protocol are the keys behind the security and privacy of the method used. The experimental results show that the accuracy and precision of the proposed algorithm, that is suitable for distributed environment, is much higher than the algorithm which works on the centralized data and in which privacy is not needed.
关键词：Data Mining; Classification; Decision Tree Classifier; Privacy Preserving Data Mining; Vertically Partitioned Data
